Date post: | 22-Dec-2015 |
Category: |
Documents |
View: | 299 times |
Download: | 1 times |
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-1
Operations Operations ManagementManagement
ForecastingForecastingChapter 4Chapter 4
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-2
What is Forecasting?What is Forecasting?
Process of predicting a future event
Underlying basis of all business decisions Production Inventory Personnel Facilities
Sales will be $200 Million!
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-3
Types of ForecastsTypes of Forecasts
Economic forecasts Address business cycle, e.g., inflation rate, money
supply etc. Technological forecasts
Predict rate of technological progress Predict acceptance of new product
Demand forecasts Predict sales of existing product
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-4
Short-range forecast Up to 1 year; usually less than 3 months Job scheduling, worker assignments
Medium-range forecast 3 months to 3 years Sales & production planning, purchasing, budgeting
Long-range forecast 3+ years New product planning, facility location
Types of Forecasts by Time HorizonTypes of Forecasts by Time Horizon
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-5
What Do We Forecast - AggregationWhat Do We Forecast - Aggregation
Clustering goods or services that have similar demand requirements and common processing, labor, and materials requirements:
Red shirts
White shirts
Blue shirts
Big Mac
Quarter Pounder
Regular Hamburger
Shirts
Pounds of Beef
$
$
Why do we aggregate?
What about units of
measurement?
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-6
Realities of ForecastingRealities of Forecasting
Forecasts are seldom perfect Most forecasting methods assume that there is
some underlying stability in the system Both product family and aggregated product
forecasts are more accurate than individual product forecasts
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-7
Persistent, overall upward or downward pattern Due to population, technology etc. Several years duration
Trend ComponentTrend Component
Time
Res
pons
e
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-8
Regular pattern of up & down fluctuations Due to weather, customs etc.
Time
Res
pons
eSeasonal ComponentSeasonal Component
Summer
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-9
Repeating up & down movements Due to interactions of factors influencing economy Usually 2-10 years duration
TimeTime
Res
pons
eR
espo
nse
Cyclical ComponentCyclical Component
Cycle
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-10
Product DemandProduct Demand
Year1
Year2
Year3
Year4
Actual demand line
Dem
and
for p
rodu
ct o
r ser
vice
Seasonal peaks Trend component
Average demand over four years
Random variation
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-11
Forecasting ApproachesForecasting Approaches
Used when situation is ‘stable’ & historical data exist Existing products Current technology
Involves mathematical techniques e.g., forecasting sales of
color televisions
Quantitative Methods Used when situation is
vague & little data exist New products New technology
Involves intuition, experience e.g., forecasting sales on
Internet
Qualitative Methods
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-12
Overview of Qualitative MethodsOverview of Qualitative Methods Jury of executive opinion
Pool opinions of high-level executives, sometimes augmented by statistical models
Delphi method Panel of experts, queried iteratively
Sales force composite Estimates from individual salespersons are reviewed
for reasonableness, then aggregated Consumer Market Survey
Ask the customer
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-13
Overview of Quantitative ApproachesOverview of Quantitative Approaches
Naïve approach Moving averages Exponential smoothing Trend projection Seasonal variation
Linear regression
Time-series Models
Associative Models
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-14
Set of evenly spaced numerical data Observing the response variable at regular time intervals
Forecast based only on past values Assumes that factors influencing the past and present will
continue to influence the future
Example Year: 1999 2000 2001 2002
2003
Sales: 78.7 63.5 89.7 93.292.1
What is a Time Series?What is a Time Series?
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-15
Naive ApproachNaive Approach
Assumes demand in next period is the same as demand in most recent period e.g., If May sales were 48, then
June sales will be 48
Sometimes cost effective & efficient
© 1995 Corel Corp.
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-16
ForecastForecast nnnn
Demand in Previous Demand in Previous PeriodsPeriods
Simple Moving AverageSimple Moving Average
F = A + A + A + A
F = A + A + A
t t–1 t–2 t–3 t–4
t t–1 t–2 t–3
4
3
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-17
ForecastForecast = = ΣΣ (Weight for period n) (Demand in period n) (Weight for period n) (Demand in period n)
ΣΣ Weights Weights
Weighted Moving AverageWeighted Moving Average
F = .4A + .3A + .2A + .1A
F = .7A + .2A + .1A
t t–1 t–2 t–3 t–4
t t–1 t–2 t–3
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-18
Form of weighted moving average Weights decline exponentially Most recent data weighted most
Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen
Involves little record keeping of past data
Exponential Smoothing MethodExponential Smoothing Method
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-19
Exponential SmoothingExponential Smoothing
F = A + (1 – ) (F )
= A + F – F
= F + (A – F )
t t–1 t–1
t–1 t–1 t–1
t–1 t–1 t–1
Forecast = (Demand last period) + (1 – ) ( Last forecast)
Forecast = Last forecast + (Last demand – Last forecast)
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-20
Tt = (Forecast this period – Forecast last period) + (1-) (Trend estimate last period)
= (Ft - Ft-1) + (1- )Tt-1
Exponential Smoothing with Exponential Smoothing with Trend AdjustmentTrend Adjustment
Forecast = Exponentially smoothed forecast (F ) + Exponentially smoothed trend (T )
t
t
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-21
Seasonal VariationSeasonal Variation
Quarter Year 1 Year 2 Year 3 Year 4
1 45 70 100 1002 335 370 585 7253 520 590 830 11604 100 170 285 215
Total 1000 1200 1800 2200 Average 250 300 450 550
Seasonal Index = Actual Demand
Average Demand = = 0.18
45
250
Forecast for Year 5 = 2600
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-22
Quarter Year 1 Year 2 Year 3 Year 4
1 45/250 = 0.18 70/300 = 0.23 100/450 = 0.22 100/550 = 0.182 335/250 = 1.34 370/300 = 1.23 585/450 = 1.30 725/550 = 1.323 520/250 = 2.08 590/300 = 1.97 830/450 = 1.84 1160/550 = 2.114 100/250 = 0.40 170/300 = 0.57 285/450 = 0.63 215/550 = 0.39
Quarter Average Seasonal Index
1 (0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20
2 (1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30
3 (2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00
4 (0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50
Seasonal VariationSeasonal Variation
Forecast
650(0.20) = 130650(1.30) = 845650(2.00) = 1300650(0.50) = 325
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-23
Overview of Quantitative ApproachesOverview of Quantitative Approaches
Naïve approach Moving averages Exponential smoothing Trend projection Seasonal variation
Linear regression
Time-series Models
Associative Models
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-24
Linear RegressionLinear Regression
Independent Dependent Variables Variable
Factors Associated with Our Sales• Advertising
• Pricing
• Competitors
• Economy
• Weather
Sales
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-25
Scatter DiagramScatter DiagramSales vs. Payroll
0
1
2
3
4
0 1 2 3 4 5 6 7 8Area Payroll (in $ hundreds of millions)
Sal
es (
in $
hu
nd
red
s o
f t
ho
usa
nd
s)
Regression Line
Now What?
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-26
Short-range forecast Up to 1 year; usually less than 3 months Job scheduling, worker assignments
Medium-range forecast 3 months to 3 years Sales & production planning, budgeting
Long-range forecast 3+ years New product planning, facility location
Types of Forecasts by Time Types of Forecasts by Time HorizonHorizon
Time Series
Associative
Qualitative
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-27
Forecast ErrorForecast Error
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-28
Forecast ErrorForecast Error
+ 5– 3
E = A – F t t t
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-29
Forecast Error - CFEForecast Error - CFE
CFE = Et
CFE – Cumulative sum of Forecast Errors
•Positive errors offset negative errors
•Useful in assessing bias in a forecast
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-30
Forecast Error - MSEForecast Error - MSE
MSE – Mean Squared Error
Accentuates large deviations
MSE = Et
n
2
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-31
Forecast Error - MADForecast Error - MAD
|Et |n
MAD =
MAD – Mean Absolute Deviation
Widely used, well understood measurement of forecast error
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-32
Forecast Error - MAPEForecast Error - MAPE
MAPE = 100|Et | / At
n
MAPE – Mean Absolute Percent Error
Relates forecast error to the level of demand
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-33
Forecast ErrorForecast Error
Et = At – Ft
CFE = Et
100 |Et | / At
nMAPE =
|Et | n
MAD =
MSE = Et
n
2
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-34
Monitoring & Controlling ForecastsMonitoring & Controlling Forecasts
We need a TRACKING SIGNAL to measure how well the forecast is predicting actual values
TS = Running sum of forecast errors (CFE) Mean Absolute Deviation (MAD)
= E
|E | / nt
t
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-35
Plot of a Tracking SignalPlot of a Tracking Signal
Time
Lower control limit
Upper control limit
Signal exceeded limitTracking signal CFE / MAD
Acceptable range
+
0
-
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-36
Forecasting in the Service SectorForecasting in the Service Sector
Presents unusual challenges special need for short term records needs differ greatly as function of industry and product issues of holidays and calendar unusual events
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-37
Forecast of Sales by Hour for Forecast of Sales by Hour for Fast Food RestaurantFast Food Restaurant
0
5
10
15
20
+11-12+1-2 +3-4 +5-6 +7-8 +9-1011-12 12-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11
© 2004 by Prentice Hall, Inc., Upper Saddle River, N.J. 074584-38
SummarySummary Demand forecasts drive a firm’s plans
- Production- Capacity- Scheduling
Need to find the forecasting method(s) that best fit our pattern of demand – no one right tool
- Qualitative methods e.g. customer surveys- Time series methods (quantitative) rely on historical demand to predict future demand
- Associative models (quantitative) use historical data on independent variables to predict demand e.g. promotional campaign
Track forecast error to determine if forecasting model requires change